Title: Estimation and Prediction of Evolving Color Distributions for Skin Segmentation Under Varying Illumination
Authors: Leonid Sigal and Stan Sclaroff
Date: December 1, 1999
Abstract:
A novel approach for real-time skin segmentation in video sequences is
described. The approach enables reliable skin segmentation despite
wide variation in illumination during tracking. An explicit second
order Markov model is used to predict evolution of the skin color
(HSV) histogram over time. Histograms are dynamically updated based
on feedback from the current segmentation and based on predictions of
the Markov model. The evolution of the skin color distribution at
each frame is parameterized by translation, scaling and rotation in
color space. Consequent changes in geometric parameterization of the
distribution are propagated by warping and re-sampling the
histogram. The parameters of the discrete-time dynamic Markov model
are estimated using Maximum Likelihood Estimation, and also evolve
over time. Quantitative evaluation of the method was conducted on
labeled ground-truth video sequences taken from popular movies.